The need to efficiently and reliably transmit and receive data at high speeds or data rates has long been known. In particular, there is a known need for multi-gigabit per second satellite links with bandwidth efficiencies of three bits per second per Hertz or greater at acceptable bit error rates. Unfortunately, there have been significant problems which have hindered the use of very high data transmission rates, including problems caused by channel distortions. In the past, equalization has been used to compensate for channel distortions introduced by band limiting atmospheric conditions and general non-ideal filtering, all of which cause intersymbol interference.
A transversal filter, e.g., a tapped delay line or nonrecursive equalizer, is one common type of equalizer which has been used to perform equalization in high data rate transmission systems. A transversal filter can be described as a tapped delay line where the output of each of a set of taps is passed through a gain, which may be adjustable, and is then summed with the other tap outputs to produce an equalized signal. In a zero forcing equalizer (ZFE), the gain of each tap output is adaptively determined by a zero forcing algorithm. In such a system, the current and time delayed values of a received signal are linearly weighted by equalizer coefficients (tap weights or gains) with the equalizer coefficients being chosen to force the signal at a feedback point (e.g., the output of the equalizer) to zero at all times other than the sampling time associated with the main path signal. At the sampling time associated with the main path signal, upon which decoding takes place, the output of the equalizer is forced to a normalized one.
FIG. 1 illustrates a known prior art transmission system, having a receiver/demodulator/decoder which uses a ZFE. In this system, a transmitter 10 converts a digital signal to, for example, symbols, modulates the symbols onto a carrier signal, using for example, a quadrature amplitude modulation (QAM) technique or any other digital modulation technique such as PSK, QPSK, etc., and transmits the modulated carrier signal through a channel 12 to a receiver/tapped delay equalizer 14. The tapped delay equalizer 14 uses tap weights to equalize the received signal and the equalized signal is then communicated to a demodulator 16 which converts the signal to baseband. An analog-to-digital (A/D) converter 18 converts the output of the demodulator 16 to a digital signal and, in most cases, separately converts the in-phase and quadrature components of the output of the demodulator 16 to digital signals. The output(s) of the A/D converter 18 are then communicated to a decision unit 20 which decodes the received symbols. In addition, the baseband signal from the demodulator 16 is communicated to a high resolution A/D converter 22 which produces a high resolution error signal. The output of the high resolution A/D converter 22 is communicated to a ZFE update calculation unit 23 which, in turn, uses the high resolution error signal to calculate tap weight updates for use in the equalizer 14. The unit 23 communicates the tap weight updates to the tapped delay equalizer 14 which adds the tap weight updates to the tap weights within the equalizer 14. As is known, the ZFE update calculation unit 23 uses a zero forcing algorithm to calculate the correlation between the error in the main path signal, as output by the A/ID converter 22, and delayed versions of that signal at a number of times delayed from the main signal path sampling time. The unit 23 uses these correlation values to determine tap weight adjustments which, when added to the tap weights within the tapped delay equalizer 14, causes the equalizer to effectively drive the signal at the feedback point 24 to zero at all sampling times except the sampling time associated with the main signal path (or the impulse response at the decision unit 20 is the same as sent by the transmitter 10). In this manner, the feedback path of the A/D converter 22 and the ZFE update calculation unit 23 automatically adapts the tap weights within the equalizer 14 to account for and negate distortions caused by changes in the channel, noise, etc., all of which can cause intersymbol interference.
While intersymbol interference can generally be corrected through equalization, current methods of equalization are relatively slow, inefficient and consume a lot of power. Furthermore, the traditional ZFE algorithm as used in the system of FIG. 1 often does not result in the best bit error rate (BER) because the common implementation of a ZFE algorithm adapts the tap weights within the tapped delay equalizer 14 to cancel intersymbol interference at the feedback point 24, not at the decision point, i.e., where symbol decoding is taking place. Thus, if, as is generally the case, the feedback point 24 is not at the end of the demodulation path, i.e., where symbol decoding is taking place, or if the feedback path itself introduces distortions, or if the tapped delay equalizer 14 has imperfections, the ZFE algorithm will not be able to adapt the tap weights in a manner that accounts for all of the distortions introduced by the elements (and only the elements) through which the signal being decoded passes. In addition, as a result of canceling intersymbol interference, noise is often added to the channel by the feedback loop. Thus, while a traditional ZFE cancels the intersymbol interference detected, this ZFE may still cause an increase in the BER over that possible.
For example, in the system of FIG. 1, the decision unit 20 performs symbol decoding on the output of the A/D converter 18 while the ZFE update calculation unit 23 makes equalizer tap weight adjustments based on the output of the A/D converter 22, which are different A/D converters. As a result, the transfer function of the A/D converter 18 is not taken into account in the ZFE update calculation unit 23 and, likewise, the transfer function of the A/D converter 22 is not taken into account by the decision unit 20, leading to a mismatch between the symbol decoding and equalizer functions. This, in turn, can lead to errors in symbol decoding and to an increased BER, which is undesirable.